Local patterns, like itemsets, correlations, contrast sets or subgroups, stand out from other data mining tools by their descriptive nature, which makes them directly interpretable by end users like clinicians, fraud experts or analysts. In this workshop, we wish to investigate typical use cases and key requirements for the successful usage of local pattern mining in applications where next to the statistical performance of models, the understandability and interestingness of the models is the key success factor.